Abstract
Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor. Mechanisms driving tumor recurrence and therapy resistance remain poorly understood, and there is an urgent need to develop novel biomarkers and therapies. To address this, we applied integrative multi-omics approaches to identify prognostic biomarkers and mechanisms of GBM aggression and recurrence. We established an integrative data fusion method for multi-omics datasets using directionality and significance estimates of genes, transcripts, and proteins, and applied it to characterize IDH1-mutant high-grade gliomas. Using this method, we integrated transcriptomes, methylomes, and proteomes of IDH1-mutant and wild-type high-grade gliomas from TCGA, GLASS, and CPTAC to uncover molecular signatures and pathways specific to IDH1-mutant gliomas while reducing false positive pathway enrichments. We then applied similar machine learning approaches to identify novel drug targets from ion channels with existing FDA-approved drugs using a large cohort of primary GBMs. We validated two novel oncogenic biomarkers, GJB2 and SCN9A, and found these genes strongly associated with poor patient prognosis, aggressive GBM subtypes, and tunneling nanotube dynamics. Functional studies demonstrated these genes regulate cell proliferation, sphere formation, and neural projections, and significantly influence tumor aggressiveness and survival in mouse models. We then focussed on mechanisms of tumor recurrence and performed short- and long-read sequencing of paired primary-recurrent GBMs, generating a deep multi-omics dataset spanning single nucleotide variants, structural variants, copy number alterations, genome-wide DNA methylation, and transcriptomics. Applying our integration method, we found multi-omics reprogramming drives glioma-specific oncogenic processes such as gliogenesis, neuropeptide signaling, and telomere maintenance. We also observed associations between tumor aggression and complex genomic rearrangements, including ecDNA amplifications of CDK4/MDM2 and EGFRvIII. This study demonstrates the power of integrative multi-omics and machine learning to discover novel biomarkers and reveal programs driving GBM aggression and recurrence, offering a roadmap for molecular diagnostics and future therapeutic development.